{"id":"W2044601776","doi":"10.1021/ja042394q","title":"Nonequilibrium Capillary Electrophoresis of Equilibrium Mixtures: A Universal Tool for Development of Aptamers","year":2005,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":298,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Aptamer; Chemistry; Capillary electrophoresis; Selection (genetic algorithm); DNA; Systematic evolution of ligands by exponential enrichment; Computational biology; Biological system; RNA; Nanotechnology; Chromatography; Computer science; Artificial intelligence; Molecular biology; Biochemistry; Gene; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00134567,0.0006178872,0.0006329637,0.0006753657,0.0003334246,0.000632913,0.001191693,0.0007323331,0.0007496214],"category_scores_gemma":[0.001265269,0.0005578729,0.0002275909,0.000295149,0.0006743238,0.0007849513,0.0005693063,0.001369465,0.0006887692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007782076,"about_ca_system_score_gemma":0.0004720143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004026564,"about_ca_topic_score_gemma":0.0006864403,"domain_scores_codex":[0.9987333,0.0002565203,0.00006443584,0.0003012524,0.000577415,0.00006706794],"domain_scores_gemma":[0.9994289,0.0002840301,0.00006875795,0.00006561568,0.0001105577,0.00004208223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008498976,0.00007009433,0.0003890905,0.0002406242,0.00002133384,0.0001105626,0.00008435435,0.001057391,0.9492451,0.009828996,0.0007216132,0.03814577],"study_design_scores_gemma":[0.00001566552,0.00007461743,0.0003385108,0.00001581483,0.000009312875,0.0003323941,0.000007701759,0.01518691,0.9713907,0.001036363,0.01157041,0.00002179205],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04168407,0.005337074,0.9477137,0.0003417547,0.0001010193,0.0001788026,0.000133635,0.001687501,0.002822418],"genre_scores_gemma":[0.287218,0.004604212,0.7001716,0.0004428623,0.00007361046,0.0004892124,0.0003262651,0.0002241173,0.006450083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00134567,"threshold_uncertainty_score":0.007116616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006530316987286731,"score_gpt":0.2525590114972763,"score_spread":0.2460286945099895,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}